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Learning JavaScript Data Structures and Algorithms

You're reading from   Learning JavaScript Data Structures and Algorithms Enhance your problem-solving skills in JavaScript and TypeScript

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Product type Paperback
Published in Jul 2026
Last Updated in Sep 2025
Publisher Packt
ISBN-13 9781836205395
Length 615 pages
Edition 4th Edition
Languages
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Author (1):
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Loiane Groner Loiane Groner
Author Profile Icon Loiane Groner
Loiane Groner
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Table of Contents (9) Chapters Close

Learning JavaScript Data Structures and Algorithms, Fourth Edition: Enhance your problem-solving skills in JavaScript and TypeScript
1 Introducing Data Structures and Algorithms in JavaScript FREE CHAPTER 2 Big O notation 3 Arrays 4 Stacks 5 Queues and Deques 6 Linked Lists 7 Sets 8 Dictionaries and Hashes

Big O time complexities

Big O notation uses capital O to denote upper bound. It signifies that the actual running time could be less than but not greater than what the function expresses. It does not tell us the exact running time of an algorithm. Instead, it tells us how bad things could get as the input size grows large.

Imagine you have a messy room and need to find a specific sock. In the worst case, you have to check each item of clothing one by one (this is like a linear time algorithm). Big O tells you that even if your room gets super messy, you will not need to look at more items than are actually there. You might get lucky and find the sock quickly! The actual time might be much less than the Big O prediction.

When analyzing algorithms, the following classifications of time and space complexities are most encountered:

Notation Name Explanation
O(1) Constant The algorithm's runtime or space usage remains the same regardless of the input size (n).
O(log(n)) Logarithmic...
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